Ford Hired Back 350+ Engineers to Fix Its Own AI’s Mistakes

There’s a lesson buried inside Ford’s best quality ranking in 16 years, and it’s one the entire auto industry should probably sit with for a moment. Ford AI quality failures — real, costly, embarrassing ones — pushed the company to do something that runs counter to every automation-first instinct in modern manufacturing: it called its old engineers back.

• Ford AI quality failures forced the company to rehire over 350 veteran engineers to correct errors made by automated systems.
• Ford AI quality problems stemmed from overconfidence in automation and the loss of irreplaceable institutional knowledge from experienced staff.
• Ford has since created a dedicated 40-person software quality assurance team and added more than 100,000 AI-powered validation tests.
• The automaker topped JD Power’s mainstream quality rankings for the first time in 16 years after overhauling its engineering approach.

A Top JD Power Ranking Built on a Hard Lesson

Ford recently claimed the number-one spot among mainstream automakers in JD Power’s Initial Quality Study — its first time at the top in 16 years. That’s a headline worth celebrating. But the more interesting story is what had to go wrong before Ford could get there.

For the better part of the last decade, Ford leaned hard into automation, AI-assisted design tools, and algorithmic decision-making in its vehicle development process. The theory was sound enough: let sophisticated systems handle complexity at scale, reduce human error, move faster. What Ford didn’t fully account for was that the effectiveness of those systems depended entirely on the quality of the data feeding them — and on whether the people who truly understood the craft of building cars were still in the building.

They weren’t. Not enough of them, anyway.

When Ford AI Quality Went Wrong

Charles Poon, Ford’s VP of vehicle hardware engineering, was unusually candid about what happened. ‘Mistakenly, we thought that by just introducing artificial intelligence and adjusting the design requirements that we had, that that would produce a high-quality product,’ he told reporters this week. That’s a striking admission from a senior executive at one of America’s largest manufacturers — essentially acknowledging that the company bet on AI and lost. The Ford AI quality misstep, in other words, came directly from the top.

The problem wasn’t that the AI tools were useless. It’s that Ford underestimated what they couldn’t do on their own. Experienced engineers carry something that can’t be easily digitised: decades of pattern recognition, instinct built across multiple vehicle programmes, and the ability to spot a problem before it becomes a recall. When those engineers left — some retiring, some moving on — their accumulated knowledge walked out with them. And Ford’s automated systems, trained on incomplete or insufficiently validated data, couldn’t compensate.

The timing made things worse. The troubled launches of the Explorer and Lincoln Aviator, supply-chain chaos during the pandemic, and a growing pile of recalls all hit around the same period. Ford currently leads the industry in total recall volume — not a distinction any automaker wants. COO Kumar Galhotra acknowledged that Ford AI quality strategy had become dangerously fragmented, with departments operating in silos and the company relying on a ‘find and fix’ philosophy that addressed defects only after they appeared.

‘We’re moving from that find-and-fix mentality to preventing issues before they occur. We’re focused on enablers and early indicators versus outputs. Stop admiring the problem and start solving it.’ — Kumar Galhotra, Ford COO

Bringing the Experts Back In

The fix, counterintuitively, was deeply human. Ford hired, promoted, or brought back more than 350 experienced engineers — veterans who’d worked through multiple vehicle development cycles and who understood where automated systems tend to go blind. Some returned specifically to retrain Ford’s AI models with better, more granular data. Others took on mentorship roles, working directly with younger engineers who were struggling to maintain quality standards without that institutional foundation beneath them.

‘That’s where some of our most experienced engineers have had experience solving and identifying those problems before they creep into the system,’ Poon said. It’s a quietly damning line. Ford AI quality controls were letting problems ‘creep into the system’ — and it took human expertise to find and root them out.

There’s a broader industry pattern here. The rush to automate engineering workflows has been widespread across automotive, aerospace, and industrial manufacturing throughout the 2010s and early 2020s. The pitch was always the same: AI can process more variables, faster, with fewer errors than people. What that pitch consistently underweighted was the value of contextual judgment — knowing not just what the data says, but what the data isn’t telling you. Ford AI quality struggles are arguably the most public, high-stakes proof point yet that automation without deep human oversight is a liability, not an asset.

Ford AI Quality Now: A Different Architecture

To its credit, Ford hasn’t responded to its AI stumbles by abandoning automation. That would be an overcorrection. Instead, the company is rebuilding its approach from the ground up — keeping the machines, but making sure the right humans are supervising them.

On the software side, Ford created a dedicated 40-person quality assurance team with a single mandate: stop problems before they ship. Historically, Ford was catching software bugs late in the development cycle — partly because it wasn’t running iterative tests aggressively enough, and partly because its software and hardware teams weren’t tightly integrated. Poon confirmed that software and digital teams now work much more closely with vehicle engineering, manufacturing, and supply-chain operations, a structural change that sounds bureaucratic but is actually significant. Maintaining strong Ford AI quality oversight is now baked into that integrated structure from the start.

Ford also dramatically expanded its automated testing infrastructure, adding more than 100,000 new AI-powered tests designed to stress software systems across a wide range of edge cases and conditions. The key advantage is speed: because the testing framework is highly automated, even a late-stage software change can be run back through the entire validation process before it reaches a customer.

‘Because these tests are highly automated, even if we have a late change in the software, we can rapidly run back through the entire validation process to guarantee it works perfectly well before it reaches the customer.’ — Charles Poon, VP of vehicle hardware engineering

Poon was also careful to draw a clear line between automotive software and consumer electronics. Ford can’t adopt a ‘move fast and fix later’ philosophy — the kind that Silicon Valley normalised for apps and smartphones. Vehicles operate in safety-critical environments. A bug in a navigation app is annoying. A bug in a braking system is catastrophic. That distinction has always existed, but it became sharper as Ford tried to marry the agility of software development with the rigour that automotive engineering demands.

What This Means for the Auto Industry

Ford AI quality failures aren’t unique to Ford — they’re a preview of tensions that every traditional automaker will navigate as software becomes a larger share of what a vehicle actually is. GM, Stellantis, Toyota, and Volkswagen are all racing to embed more AI into their engineering and manufacturing workflows. They’d be wise to study what happened in Dearborn.

The automakers that come out ahead won’t be the ones who automate the most, or the fastest. They’ll be the ones who figure out where human judgment is genuinely irreplaceable — and who build systems that augment it rather than quietly replace it. Ford’s 350 rehired engineers aren’t a retreat from the future. They’re proof that the future still needs them.

Source: The Verge

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